Harnessing AI and computing to advance climate modelling and prediction

Tapio Schneider (), Swadhin Behera, Giulio Boccaletti, Clara Deser, Kerry Emanuel, Raffaele Ferrari, L. Ruby Leung, Ning Lin, Thomas Müller, Antonio Navarra, Ousmane Ndiaye, Andrew Stuart, Joseph Tribbia and Toshio Yamagata
Additional contact information
Tapio Schneider: California Institute of Technology
Swadhin Behera: Japan Agency for Marine-Earth Science and Technology
Giulio Boccaletti: Centro Euro-Mediterraneo sui Cambiamenti Climatici
Clara Deser: National Center for Atmospheric Research
Kerry Emanuel: Massachusetts Institute of Technology
Raffaele Ferrari: Massachusetts Institute of Technology
L. Ruby Leung: Pacific Northwest National Laboratory
Ning Lin: Princeton University
Thomas Müller: University of Konstanz
Antonio Navarra: Centro Euro-Mediterraneo sui Cambiamenti Climatici
Ousmane Ndiaye: National Agency for Civil Aviation and Meteorology
Andrew Stuart: California Institute of Technology
Joseph Tribbia: National Center for Atmospheric Research
Toshio Yamagata: Japan Agency for Marine-Earth Science and Technology

Nature Climate Change, 2023, vol. 13, issue 9, 887-889

Abstract: There are contrasting views on how to produce the accurate predictions that are needed to guide climate change adaptation. Here, we argue for harnessing artificial intelligence, building on domain-specific knowledge and generating ensembles of moderately high-resolution (10–50 km) climate simulations as anchors for detailed hazard models.

Date: 2023
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DOI: 10.1038/s41558-023-01769-3

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